tf.compat.v1.tpu.rewrite
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Rewrites computation
for execution on a TPU system.
tf.compat.v1.tpu.rewrite(
computation, inputs=None, infeed_queue=None, device_assignment=None, name=None
)
Args |
computation
|
A Python function that builds a computation to apply to the
input. If the function takes n inputs, 'inputs' should be a list of n
tensors.
computation may return a list of operations and tensors. Tensors must
come before operations in the returned list. The return value of
rewrite is a list of tensors corresponding to the tensors from the
output of computation .
All Operation s constructed during computation will be executed when
evaluating any of the returned output tensors, not just the ones returned.
|
inputs
|
A list of input tensors or None (equivalent to an empty list).
Each input can be a nested structure containing values that are
convertible to tensors. Note that passing an N-dimension list of
compatible values will result in a N-dimension list of scalar tensors
rather than a single Rank-N tensors. If you need different behavior,
convert part of inputs to tensors with tf.convert_to_tensor .
|
infeed_queue
|
If not None , the InfeedQueue from which to append a tuple
of arguments as inputs to computation .
|
device_assignment
|
if not None , a DeviceAssignment describing the
mapping between logical cores in the computation with physical cores in
the TPU topology. May be omitted for a single-core computation, in which
case the core attached to task 0, TPU device 0 is used.
|
name
|
(Deprecated) Does nothing.
|
Returns |
Same data structure as if computation(*inputs) is called directly with some
exceptions for correctness. Exceptions include:
1) None output: a NoOp would be returned which control-depends on
computation.
2) Single value output: A tuple containing the value would be returned.
3) Operation-only outputs: a NoOp would be returned which
control-depends on computation.
|
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Last updated 2020-10-01 UTC.
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